Covariate Adjustment in Bayesian Adaptive Randomized Controlled Trials

TL;DR

This paper explores covariate adjustment in Bayesian adaptive randomized controlled trials, demonstrating that such adjustment enhances trial power, reduces sample size, and increases early stopping probability through simulation and real-world COVID-19 trial application.

Contribution

It characterizes covariate adjustment within Bayesian adaptive designs, including methods for obtaining marginal estimands and assessing their impact through simulations and real data.

Findings

Covariate adjustment increases trial power.

Adjustment reduces expected sample sizes.

It enhances early stopping probability.

Abstract

In conventional randomized controlled trials, adjustment for baseline values of covariates known to be at least moderately associated with the outcome increases the power of the trial. Recent work has shown particular benefit for more flexible frequentist designs, such as information adaptive and adaptive multi-arm designs. However, covariate adjustment has not been characterized within the more flexible Bayesian adaptive designs, despite their growing popularity. We focus on a subclass of these which allow for early stopping at an interim analysis given evidence of treatment superiority. We consider both collapsible and non-collapsible estimands, and show how to obtain posterior samples of marginal estimands from adjusted analyses. We describe several estimands for three common outcome types. We perform a simulation study to assess the impact of covariate adjustment using a variety of adjustment models in several different scenarios. This is followed by a real world application of the compared approaches to a COVID-19 trial with a binary endpoint. For all scenarios, it is shown that covariate adjustment increases power and the probability of stopping the trials early, and decreases the expected sample sizes as compared to unadjusted analyses.